Assessment of liver fibrosis scoring with serum marker algorithms

ABSTRACT

The present invention concerns a method for diagnosing liver fibrosis wherein two or more diagnostic markers are measured and the measurements are correlated by a mathematic algorithm characterized in that the diagnostic markers are selected from the group N-terminal procollagen III propeptide (PIIINP), Collagen IV/, Collagen VI, Tenascin, Laminin, Hyaluronan, MMP-2, TIMP-1 and MMP-9/TIMP complex. The algorithm can be used to predict the histological score of a liver biopsy.

[0001] Progressive fibrotic diseases of the liver are a major cause ofmorbidity and mortality throughout the world. Recent scientific advancesdemonstrate that the pathogenic process of fibrosis in liver iscritically dependent on proliferation and activation of hepatic stellatecells (also called lipocytes, fat-storing or Ito cells) which synthesizeand secrete excess extracellular matrix proteins (1). Moreover it isevident that this process is common to liver disease of all aetiologies.Of particular importance are chronic viral hepatitis B and C andalcoholic liver disease as well as autoimmune and genetic liverdiseases, all of which lead to clinical problems via the common finalpathway of progressive liver fibrosis, with the eventual development ofcirrhosis.

[0002] An important concept is the distinction between hepatic fibrosisand cirrhosis. Hepatic fibrosis is a reversible accumulation ofextracellular matrix in response to chronic injury in which nodules havenot yet developed, whereas cirrhosis implies an irreversible process, inwhich thick bands of matrix fully encircle the parenchyma, formingnodules. Consequently, any therapy must be directed towards patientswith reversible disease (fibrosis), which will require earlyidentification and monitoring of those at risk (2).

[0003] Severity and progression of liver fibrosis are difficult toassess, with liver biopsy currently remaining the most reliable clinicalmethod. The qualitative evaluation of hepatic fibrosis by biopsies islimited by interobserver variability. Biopsies are clearly inadequatefor the early clinical phase of drug development, where there is animperative to-employ less invasive methods that identify effectivecompounds within a commercially acceptable time frame, usually measuredin weeks to a maximum of three months of experimental therapeuticexposure. Further disadvantages are the low diagnostic specificity andthe risk of bleeding. Therefore there is a need for surrogate markers ofliver fibrosis. Serum tests allow a non-invasive assessment offibrogenesis and fibrolysis in the liver and can be done repeatedly andat short time intervals (3). Serum tests measuring the dynamic processesof extracellular matrix synthesis (fibrogenesis) and extracellularmatrix degradation (fibrolysis) reflect the amount of extracellularmatrix present, the degree of fibrosis or the ongoing process ofarchitectural change of the liver (4).

[0004] The current state of the art in measuring surrogate markers ofliver fibrosis is poorly developed. Previous studies have suggested thatserum levels of extracellular matrix proteins (or their cleavagefragments) may be used to assess the severity and progression of liverfibrosis (4.5, U.S. Pat. No. 5,316,914, EP 0 283 779). Different serummarkers have been investigated and correlations with liver biopsies andand severity of liver diseases have been found (6).

[0005] Some of the makers used for the assessment of liver fibrosis arePIIINP, Laminin, Hyaluronan, Collagen IV, TIMP-1, Tenascin, MMP-2 andFibronectin. Markers are measured and their capability to assess liverfibrosis has been shown. Nevertheless, the diagnostic values of eachsingle marker is not accurate and specific to assess fibrosis scores.

[0006] Therefore combinations of markers are discussed to increase thediagnostic value the simple biological index PGA combining prothrombintime (PT), serum gamma-glutamyl transpeptidase (GGT) and apolipoproteinA1 (Apo A1) and the index PGAA which includes alpha-2-macroglobulin(A₂M) to the PGA index have been described for the diagnosis ofalcoholic liver disease in drinkers (7,8). Although the PGA and PGAAindex have been combined with single serum markers (9, 10) serum markershave not been used for establishing algorithms for the assessment ofliver diseases.

[0007] In this invention serum markers of the extracellular matrix areassembled together in a panel leading to a set of markers whosemeasurement will enable the calculation of an algorithm and the use ofsuch a derived algorithm for the prediction of liver fibrosishistological score. For this purpose discriminant function analysis isused to determine which variables discriminate between the differentfibrosis scores. The algorithms are derived from the set of markersN-terminal procollagen III propeptide (PIIINP), Collagen IV, CollagenVI, Tenascin, Laminin, Hyaluronan, MMP-2, TIMP-1 and M-9/TIMP-1 complex:

[0008] Generally, all new techniques have to be validated againstexisting standard techniques, provided there are any. The current“gold-standard” to assess fibrosis in the liver is the liver biopsy.With the biopsy, some randomly taken tissue out of the liver is cut intoslices which become examined by an expert using a microscope. There area lot of problems associated with liver biopsies inducing someuncertainty: distribution of fibrosis in the liver (clustered fibrosisand the needle might have hit regions of the liver not affected byfibrosis), failed sample preparation (e.g. not enough tissue material),and pathologist's individuality and preferences (individualassessments). Furthermore, the fibrotic state of the liver is usuallydescribed using scores and there are a lot of different, possiblyincompatible scoring systems (e.g. Scheuer Score, Ishak Scores, etc.).

[0009] For example, in a study with 24 patients, two independentpathologists had to score the same biopsy samples for each patient attwo different time-points using two different scoring systems. Thenumber of assessments where the two pathologists came to identicalresults ranged from only 36% to 46%.

[0010] The new technique is based on measuring serum parameters, whichare directly associated with the fibrotic process, and combining them ona mathematical level yielding a fixed assessment procedure.

[0011] In order to validate the new technique the “gold-standard” is notthe best but the only mean, since a priori both methods do notinvestigate comparable endpoints: whereas the serum parameterscharacterize dynamic processes, the biopsy characterizes the fibroticmanifestation at a fixed time-point. There may be a highly activefibrotic process in the liver although fibrotic tissue has not yet beendeveloped. In contrast, there may be large clusters of fibrotic tissuein the liver but the fibrotic activity stopped or discontinuedtemporarily.

[0012] Although, some mathematical functions of serum parameters yieldedstatistically significant different mean values in different biopsyscore stages. Discriminant analyses using the “gold-standard” werechosen in order to investigate the diagnostic power of thosemathematical functions of serum parameters.

[0013] Determination of concentrations of serum markers and subsequentcalculation of an algorithm can also be used to make decisions whetheror not a biopsy has to be taken and whether treatment should be startedor continued or stopped. Therefore assignment of patients into a groupof biopsy scores without taken a biopsy is advantageous. Categorizationof patients into groups, e.g. mild versus serious fibrosis, by usingalgorithms is a benefit of the invention described.

DESCRIPTION OF THE IMMUNOASSAYS

[0014] The markers N-terminal procollagen III propeptide (PIIINP),Collagen IV, Collagen VI, Tenascin, Laminin, Hyaluronan, MMP-2, TIMP-1and MMP-9/TIMP-1 complex are used for algorithms.

[0015] The markers are measured by making use of sandwich immunoassays.The immunoassays of the invention comprises reaction of two antibodieswith human fluid samples, wherein the capture antibody specificallybinds to one epitope of the marker. The second antibody of differentepitope specificity is used to detect this complex. Preferably theantibodies are monoclonal antibodies and both antibodies of said twoantibodies of the assay specifically bind to the protein.

[0016] Antibody or other similar term used herein includes a wholeimmunoglobulin either monoclonal or polyclonal as well as antigenicfragments or immunoreactive fragments which specifically bind to themarker, including Fab, Fab′, F(ab′)₂ and F(v). Antibody includes alsobinding-proteins, especially Hyaluronic acid binding protein (HABP).

[0017] The human fluid samples used in the assays of the invention canbe any samples that contain the markers, e.g. blood, serum, plasma,urine, sputum or broncho alveolar lavage (BAL). Typically a serum orplasma sample is employed.

[0018] Antibodies of the invention can be prepared by techniquesgenerally known in the art, and are typically generated to a sample ofthe markers.

[0019] The second antibody is conjugated to a detector group, e.g.alkaline phosphatase, horseradish peroxidase, or a fluorescence dye.Conjugates are prepared by techniques generally known in the art.

[0020] Concentration of the markers in human fluids are measured andalgorithms calculated to assess the degree of fibrosis.

STATISTICAL BACKGROUND

[0021] Discriminant function analysis is used to determine whichvariables discriminate between two or more naturally occurring groups.Computationally, it is very similar to analysis of variance. The basicidea underlying discriminant function analysis is to determine whethergroups differ with regard to the mean of a variable, and then to usethat variable to predict group membership (e.g., of new cases). Statedin this manner, the discriminant function problem can be rephrased as aone-way analysis of variance (ANOVA) problem. Specifically, one can askwhether or not two or more groups are significantly different from eachother with respect to the mean of a particular variable. If the meansfor a variable are significantly different in different groups, then wecan say that this variable discriminates between the groups. In the caseof a single variable, the final significance test of whether or not avariable discriminates between groups is the F test. F is essentiallycomputed as the ratio of the between-groups variance in the data overthe pooled (average) within-group variance. If the between-groupvariance is significantly larger then there must be significantdifferences between means.

[0022] Usually, one includes several variables in a study in order tosee which one(s) contribute to the discrimination between groups. Inthat case, we have a matrix of total variances and co-variances;likewise, we have a matrix of pooled within-group variances andco-variances. We can compare those two matrices via multivariate F testsin order to determined whether or not there are any significantdifferences (with regard to all variables) between groups. Thisprocedure is identical to multivariate analysis of variance or MANOVA Asin MANOVA, one could first perform the multivariate test, and, ifstatistically significant, proceed to see which of the variables havesignificantly different means across the groups. Thus, even though thecomputations with multiple variables are more complex, the principalreasoning still applies, namely, that we are looking for variables thatdiscriminate between groups, as evident in observed mean differences.

[0023] For a set of observations containing one or more quantitativevariables and a classification variable defining groups of observations,the discrimination procedure develops a discriminant criterion toclassify each observation into one of the groups. Post hoc predicting ofwhat has happened in the past is not that difficult. It is not uncommonto obtain very good classification if one uses the same cases from whichthe discriminant criterion was computed. In order to get an idea of howwell the current discriminant criterion “performs”, one must classify (apriori) different cases, that is, cases that were not used to estimatethe discriminant criterion. Only the classification of new cases allowsus to assess the predictive validity of the discriminant criterion. Inorder to validate the derived criterion, the classification can beapplied to other data sets. The data set used to derive the discriminantcriterion is called the training or calibration data set.

[0024] The discriminant criterion (function(s) or algorithm), isdetermined by a measure of generalized squared distance. It can be basedon the pooled co-variance matrix yielding a linear function. EitherMahalanobis or Euclidean distance can be used to determine proximity.

[0025] For the development of a discriminant algorithm, data of a groupof subjects of an observational liver fibrosis of study were analyzed.Liver fibrosis scoring systems under view were

[0026] the Scheuer Score (0-4),

[0027] the Modified Ishak Score (HAI) A—Interface Hepatitis (0-4),

[0028] the Modified Ishak Score (HAI) B—Confluent Necrosis (0-6),

[0029] the Modified Ishak Score (HAI) C—Spotty Necrosis (0-4),

[0030] the Modified Ishak Score (HAI) D—Portal Inflammation (0-4),

[0031] the Modified HAI Score (Ishak Score)(0-6).

[0032] Applying a stepwise discriminant analysis, for example thefollowing functions of serum parameters showed to have major impact onthe corresponding scoring type. Scoring Type Surrogate ParametersScheurer Score: ln(TIMP -1) ln(Collagen VI/ ln(Hyaluronan/ Hyaluronan)Laminin) Modified Ishak Score A - Interface Hepatitis: ln(TIMP-1)ln(Collagen VI/ ln(Collagen VI/ Hyaluronan) Tenascin) Modified IshakScore B - Confluent Necrosis: ln(Hyaluronan) ln(Collagen VI/ MMP-2)Modified Ishak Score C - Spotty Necrosis: ln(Hyaluronan)ln(MMP-9/TIMP-1/ complex Tenascin) Modified Ishak Score D - PortalInflammation: ln(Laminin) ln(Collagen VI/ TIMP-1) Modified Ishak Score -Stages ln(TIMP-1) ln(Collagen VI/ ln(Hyaluronan/ Hyaluronan) Laminin)

[0033] A corresponding discriminant analysis yielded the lineardiscriminating functions which can be used for calculation andprediction of biopsy score. The algorithms can be applied to every knownscoring system (e.g. Scheuer Score, Ishak Score, Netavir Score, LudwigScore, HAI Score).

[0034] Algorithms can be used to predict the biopsy score of a patient(e.g. score 0, 1, 2, 3, . . . ) or to predict a group of scores(category) a patient belongs to (e.g. mild fibrosis; score 0 to 1).

[0035] Discriminating functions used includes combinations of markersfrom the list of N-terminal procollagen m propeptide (PIIINP), CollagenIV, Collagen VI, Tenascin, Laminin, Hyaluronan, MMP-2, TIMP-1 andMMP-9/TIMP-1 complex and also factors between −1000 and +1000.

[0036] Different scores need different algorithm form the list ofmarkers and factors.

EXAMPLES Example 1

[0037] Algorithms for Scheuer Score

[0038] The following algorithms 1, 2 and 3 were calculated bycorrelating biopsies assessed by the Scheuer scoring system and serummarker concentrations of a group of patients with liver diseases: [0]−108.861 + 0.283 * LOG(COL_VI/HYAL) − 1.050 * LOG(HYAL/LAM) + 35.372 *LOG(TIMP1) [1] −114.231 + 0.195 * LOG(COL_VI/HYAL) − 0.654 *LOG(HYAL/LAM) + 36.158 * LOG(TIMP1) [2] −120.649 − 0.998 *LOG(COL_VI/HYAL) − 2.102 * LOG(HYAL/LAM) + 36.925 * LOG(TIMP1) [3]−123.672 − 1.281 * LOG(COL_VT/HYAL) − 1.344 * LOG(HYAL/LAM) + 37.163 *LOG(TIMP1) [4] −133.207 − 2.186 * LOG(COL_VI/HYAL) − 1.602 *LOG(HYAL/LAM) + 38.188 * LOG(TIMP1)

[0039] [0] −75.18797 + 23.04542 * LOG(TIMP1) − 0.583641 *LOG(COL_VI/HYAL) − 0.140956 * LOG(HYAL/LAM) [1] −76.1526 + 23.15895 *LOG(TIMP1) − 0.963402 * LOG(COL_VI/HYAL) − 0.009472 * LOG(HYAL/LAM) [2]−78.62662 + 23.32161 * LOG(TIMP1) − 1.227332 * LOG(COL_VI/HYAL) −0.067969 * LOG(HYAL/LAM) [3] −83.09285 + 23.64493 * LOG(TIMP1) −2.181493 * LOG(COL_VI/HYAL) − 0.300241 * LOG(HYAL/LAM) [4] −93.89732 +24.86246 * LOG(TIMP1) − 2.841299 * LOG(COL_VI/HYAL) − 0.136885 *LOG(HYAL/LAM)

[0040] [0] −95.39661 + 17.66025 * LOG(HYAL) − 0.820836 * LOG(COL_IV) +0.245778 * LOG(COL_VI/PIIINP) − 17.79663 * LOG (COL_VI/TIMP1) −14.96754 * LOG(HYAL/MMP2) − 0.279356 * LOG(LAM/MMP9T) [1] −95.84457 +17.62365 * LOG(HYAL) − 0.667854 * LOG(COL_IV) + 0.155707 *LOG(COL_VI/PIIINP) − 18.0407 * LOG (COL_VI/TIMP1) − 14.42688 *LOG(HYAL/MMP2) − 0.554323 * LOG(LAM/MMP9T) [2] −99.13575 + 17.76656 *LOG(HYAL) − 0.978731 * LOG(COL_IV) − 0.12995 * LOG(COL_VI/PIIINP) −18.69948 * LOG (COL_VI/TIMP1) − 14.49353 * LOG(HYAL/MMP2) − 0.647247 *LOG(LAM/MMP9T) [3] −104.4554 + 18.38886 * LOG(HYAL) − 0.202832 *LOG(COL_IV) − 0.157058 * LOG(COL_VI/PIIINP) − 18.70409 * LOG(COL_VI/TIMP1) − 14.49716 * LOG(HYAL/MMP2) − 0.340197 * LOG(LAM/MMP9T)[4] −119.8887 + 20.14719 * LOG(HYAL) + 0.959792 * LOG(COL_IV) −0.80876 * LOG(COL_VI/PIIINP) − 18.69873 * LOG (COL_VI/TIMP1) −15.57103 * LOG(HYAL/MMP2) − 0.229757 * LOG(LAM/MMP9T)

[0041] The algorithm were used to predict biopsy scores of a separategroup of patients. The calculated scores were compared with scoresdetermined by a single pathologist (case B), with a consensus score of 3pathologists (case C) and with the range covered by all pathologists(case A). Kappa values, negative predictive values (NPV) for score 0-1,positive predictive values (PPV) for score 2-4, sensitivities andspecificities have also been calculated. Algorithm 1 Algorithm 2Algorithm 3 C A B C A B C A B Hit-Rate (%) [0] 33.3 38.9 35.0 17.1 40.013.7 20.0 41.4 16.8 Hit-Rate (%) [1] 36.8 42.7 36.0 80.7 81.6 75.8 74.677.2 71.7 Hit-Rate (%) [2] 25.8 42.4 19.0 0.0 36.8 0.0 0.0 34.2 5.1Hit-Rate (%) [3] 26.1 34.8 22.2 6.4 17.0 5.2 12.8 21.3 9.3 Hit-Rate (%)[4] 63.0 63.0 55.9 62.5 62.5 52.9 43.8 43.8 47.1 Hit-Rate (%) All 35.942.9 33.8 42.2 54.2 36.7 39.5 51.2 36.7 N 468 468 793 301 301 626 301301 626 Kappa 0.175 0.268 0.151 . 0.199 . 0.124 0.310 0.121 L_Kappa0.119 0.211 0.109 . 0.134 . 0.056 0.235 0.077 U_Kappa 0.231 0.325 0.192. 0.265 . 0.191 0.385 0.165 P (Kappa = 0) <0.0001 <0.0001 <0.0001 .<0.0001 . <0.0001 <0.0001 <0.0001 NPV (%) [0-1] 61.6 63.8 62.8 91.8 92.485.5 88.0 89.1 81.6 PPV (%) [2-4] 66.1 75.1 66.3 31.6 46.2 35.4 35.048.7 39.0 Hit-Rate (%) All 63.5 68.4 64.2 68.4 74.4 65.8 67.4 73.4 64.9Kappa 0.268 0.372 0.280 0.261 0.417 0.226 0.252 0.404 0.219 L_Kappa0.182 0.291 0.214 0.160 0.315 0.152 0.146 0.299 0.142 U_Kappa 0.3540.454 0.346 0.363 0.520 0.300 0.358 0.508 0.295 P (Kappa = 0) <0.0001<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001Sensitivity 0.539 0.584 0.540 0.712 0.794 0.613 0.651 0.740 0.578Specificity 0.729 0.791 0.739 0.679 0.730 0.671 0.681 0.732 0.674

Example 2

[0042] Algorithms for Ishak Score

[0043] The following algorithms 1, 2 and 3 were calculated bycorrelating biopsies assessed by the Ishak scoring system and serummarker concentrations of a group of patients with liver diseases: [0]−107.752 − 0.347 * LOG(COL_VI/HYAL) − 1.493 * LOG(HYAL/LAM) + 34.879 *LOG(TIMP1) [1] −112.550 − 0.301 * LOG(COL_VI/HYAL) − 1.086 *LOG(HYAL/LAM) + 35.617 * LOG(TIMP1) [2] −114.626 − 0.760 *LOG(COL_VI/HYAL) − 1.270 * LOG(HYAL/LAM) + 35.819 * LOG(TIMP1) [3]−121.339 − 2.065 * LOG(COL_VI/HYAL) − 2.910 * LOG(HYAL/LAM) + 36.593 *LOG(TIMP1) [4] −119.289 − 1.009 * LOG(COL_VI/HYAL) − 1.271 *LOG(HYAL/LAM) + 36.449 * LOG(TIMP1) [5] −125.551 − 2.966 *LOG(COL_VI/HYAL) − 2.536 * LOG(HYAL/LAM) + 36.797 * LOG(TIMP1) [6]−133.055 − 3.256 * LOG(COL_VI/HYAL) − 2.329 * LOG(HYAL/LAM) + 37.695 *LOG(TIMP1)

[0044] [0] −75.94035 + 23.20826 * LOG(TIMP1) − 0.911827 *LOG(COL_VI/HYAL) − 0.295297 * LOG(HYAL/LAM) [1] −76.0885 + 23.14058 *LOG(TIMP1) − 1.221511 * LOG(COL_VI/HYAL) − 0.155608 * LOG(HYAL/LAM) [2]−80.17664 + 23.6506 * LOG(TIMP1) − 1.41651 * LOG(COL_VI/HYAL) −0.210415 * LOG(HYAL/LAM) [3] −79.12945 + 23.42277 * LOG(TIMP1) −1.582733 * LOG(COL_VI/HYAL) − 0.175959 * LOG(HYAL/LAM) [4] −83.24617 +23.7777 * LOG(TIMP1) − 2.174834 * LOG(COL_VI/HYAL) − 0.311583 *LOG(HYAL/LAM) [5] −89.60186 + 24.2615 * LOG(TIMP1) − 3.237993 *LOG(COL_VI/HYAL) − 0.914309 * LOG(HYAL/LAM) [6] −95.5774 + 25.11333 *LOG(TIMP1) − 3.293235 * LOG(COL_VI/HYAL) − 0.347014 * LOG(HYAL/LAM)

[0045] [0] −100.6452 + 17.18813 * LOG(HYAL) + 15.20461 *LOG(COL_IV/HYAL) + 0.515498 * LOG(COL_VI/PIIINP) + 3.309452 * LOG (LAM)− 15.47806 * LOG(COL_IV/MMP2) − 17.50773 * LOG(COL_VI/TIMP1) [1]−98.87092 + 17.18161 * LOG(HYAL) + 14.7876 * LOG(COL_IV/HYAL) +0.530071 * LOG(COL_VI/PIIINP) + 3.067209 * LOG (LAM) − 14.74001 *LOG(COL_IV/MMP2) − 17.62455 * LOG(COL_VI/TIMP1) [2] −104.8869 +17.78543 * LOG(HYAL) + 15.25944 * LOG(COL_IV/HYAL) + 0.352181 *LOG(COL_VI/PIIINP) + 3.175207 * LOG (LAM) − 15.56044 * LOG(COL_IV/MMP2)− 17.97986 * LOG(COL_VI/TIMP1) [3] −102.8131 + 17.32281 * LOG(HYAL) +14.69307 * LOG(COL_IV/HYAL) + 0.176959 * LOG(COL_VI/PIIINP) + 2.822227 *LOG (LAM) − 15.15272 * LOG(COL_IV/MMP2) − 18.37351 * LOG(COL_VI/TIMP1)[4] −109.2574 + 18.44309 * LOG(HYAL) + 15.53464 * LOG(COL_IV/HYAL) −0.152374 * LOG(COL_VI/PIIINP) + 2.957847 * LOG (LAM) − 15.02773 *LOG(COL_IV/MMP2) − 18.59138 * LOG(COL_VI/TIMP1) [5] −116.8556 +19.00778 * LOG(HYAL) + 15.47539 * LOG(COL_IV/HYAL) + 0.436656 *LOG(COL_VI/PIIINP) + 3.995456 * LOG (LAM) − 15.54302 * LOG(COL_IV/MMP2)− 18.53013 * LOG(COL_VI/TIMP1) [6] −127.2084 + 21.66093 * LOG(HYAL) +17.77795 * LOG(COL_IV/HYAL) − 0.631902 * LOG(COL_VI/PIIINP) + 3.589129 *LOG (LAM) − 16.1393 * LOG(COL_IV/MMP2) − 18.40445 * LOG(COL_VI/TIMP1)

[0046] The algorithms were used to predict biopsy scores of a separategroup of patients. The calculated scores were compared with scoresdetermined by a single pathologist (case B), with a consensus score of 3pathologists (case C) and with the range covered by all pathologists(case A). Kappa values, negative predictive values (NPV) for score 0-2,positive predictive values (PPV) for score 3-6, sensitivities andspecificities have also been calculated. Algorithm 1 Algorithm 2Algorithm 2 C A B C A B C A B Hit-Rate (%) [0] 28.7 31.5 29.5 45.7 58.641.0 45.7 57.1 39.8 Hit-Rate (%) [1] 25.0 34.0 29.2 50.8 60.7 50.8 27.941.0 37.7 Hit-Rate (%) [2] 10.7 24.0 9.7 0.0 22.4 1.1 1.7 15.5 1.1Hit-Rate (%) [3] 23.0 27.9 20.2 0.0 3.1 0.0 9.4 12.5 6.9 Hit-Rate (%)[4] 22.2 37.8 25.6 0.0 0.0 0.0 0.0 7.7 3.3 Hit-Rate (%) [5] 32.0 44.024.4 4.5 18.2 2.6 0.0 18.2 2.6 Hit-Rate (%) [6] 57.4 57.4 51.1 71.9 71.960.9 43.8 43.8 52.2 Hit-Rate (%) All 27.1 34.6 27.3 28.9 39.5 28.1 22.332.6 25.2 N 468 468 794 301 301 627 301 301 627 Kappa 0.138 0.228 0.136. . . 0.031 0.093 0.041 L_Kappa 0.090 0.177 0.100 . . . −0.02 0.0390.006 U_Kappa 0.186 0.279 0.173 . . . 0.084 0.147 0.076 P (Kappa = 0)<0.0001 <0.0001 <0.0001 . . . 0.2293 0.0003 0.0152 NPV (%) [0-2] 57.259.4 59.1 89.9 91.0 83.9 77.8 80.4 75.1 PPV (%) [3-6] 71.9 79.5 74.240.2 50.9 41.5 47.3 55.4 48.1 Hit-Rate (%) All 63.0 67.3 65.0 71.4 76.167.6 66.4 71.1 64.8 Kappa 0.274 0.362 0.312 0.330 0.450 0.271 0.2590.366 0.238 L_Kappa 0.191 0.283 0.249 0.223 0.346 0.195 0.147 0.2570.160 U_Kappa 0.356 0.441 0.374 0.437 0.554 0.347 0.371 0.475 0.316 P(Kappa = 0) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001<0.0001 <0.0001 Sensitivity 0.524 0.561 0.537 0.703 0.770 0.617 0.5580.626 0.547 Specificity 0.757 0.816 0.781 0.717 0.758 0.697 0.714 0.7520.699

Example 3

[0047] Receiver Operating Characteristic (ROC) Curves for Scheuer Score

[0048] Grouping the patients into categories no/mild fibrosis (score0-1) and moderate/severe fibrosis (score 2-4) for the Scheuer score andcalculating algorithms for the dichotomous outcome gave the followingresults: LOGIT = 7.11957755 − 0.67952658LOG(TIMP1) + 1.01832374 *LOG(COL_VI/HYAL) + 0.09461778 * LOG(HYAL/LAM)

[0049] LOGIT = 8.6908419 − 0.76944684 * LOG(HYAL) − 0 0.47836706 *LOG(COL_IV) + 0.43870798 * LOG(COL_VI/PIIINP) + 0.74453459 * LOG(COL_VI/TIMP1) + 0.05605262 * LOG(HYAL/MMP2) − 0.01871531 *LOG(LAM/MMP9T)

[0050] The algorithms were used to calculate receiver operatingcharacteristic curves for the categories no/mild fibrosis (score 0-1)and moderate/severe fibrosis (score 2-4) for the Scheuer score. Thecalculated scores were compared with scores determined by a singlepathologist (case B), with a consensus score of 3 pathologists (case C)and with the range covered by all pathologists (case A). Area undercurve (AUC) values have been calculated. Algorithm 4 Algorithm 5 C A B CA B AUC 0.759 0.899 0.759 0.746 0.871 0.756 N 295 295 569 291 291 562

Example 4

[0051] Receiver Operating Characteristic (ROC) Curves for Ishak Score

[0052] Grouping the patients into categories no/mild fibrosis (score0-2) and moderate/severe fibrosis (score 3-6) for the Ishak score andcalculating algorithms for the dichotomous outcome gave the followingresults: LOGIT = 7.22920269-0.68033581*LOG(TIMP1)+1.04300795*LOG(COL_VI/HYAL)+0.08483109*LOG(HYAL/LAM)

[0053] LOGIT= 8.92321331-1.28340678*LOG(HYAL)−0.54350583*LOG(COL_IV/HYAL)+0.47836792*LOG(COL_VI/PIIINP)+0.02076678*LOG(LAM)+0.07719237*LOG(COL_IV/MMP2)+0.76194671*LOG (COL_VI/TIMP1)

[0054] The algorithms were used to calculate receiver operatingcharacteristic curves for the categories no/mild fibrosis (score 0-2)and moderate/severe fibrosis (score 3-6) for the Ishak score. Thecalculated scores were compared with scores determined by a singlepathologist (case B), with a consensus score of 3 pathologists (case C)and with the range covered by all pathologists (case A). Area undercurve (AUC) values have been calculated. Algorithm 4 Algorithm 5 C A B CA B AUC 0.763 0.887 0.763 0.751 0.861 0.757 N 295 295 570 292 292 564

[0055] Literature

[0056] 1. Friedman S L The cellular basis of hepatic fibrosis:Mechanismand treatment strategies. N Engl J Med 1993; 328: 1828-1835

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1. A method for diagnosing liver fibrosis wherein two or more diagnosticmarkers are measured in a body fluid and the measurements are combinedby mathematical algorithms.
 2. A method according to claim 1 wherein thealgorithms are characterized in a way that the diagnostic markers areselected from the group of N-terminal procollagen III propeptide(PIIINP), Collagen IV, Collagen VI, Tenascin, Laminin, Hyaluronan,MMP-2, TIMP-1 and MMP-9 TIMP-1 complex.
 3. A method according to claim 1wherein body fluid is blood, serum, plasma or urin.
 4. A methodaccording to claim 1 and 2 wherein the algorithms can be used tosupport, predict or substitute the histological score of a liver biopsy.5. A method according claim 4 wherein the algorithms can be adjuted tomatch known scoring system (e.g. Scheuer score, Ishak score, HAI score,Ludwig score, Metavir score).
 6. A method according to claim 1 and 2wherein the algorithms can be used to categorize patients into groups ofscores.
 7. A method according to claim 4 and 6 wherein treatmentdecisions can be supported by the calculated score or the calculatedscore group.
 8. A method according to claim 4 and 6 wherein clinicaldrug trials for liver diseases including dose-finding studies can besupported by the calculated score or the calculated score group.
 9. Amethod according to claim 4 and 6 wherein decisions whether or not abiopsy should be taken can be supported by the calculated score or thecalculated score group.
 10. A method according claim 6 wherein patientscan be classified as suffering from no/mild or moderate/severe liverfibrosis.